A generalized computer vision approach to mapping crop fields in heterogeneous agricultural landscapes
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Thomas J. Fuchs | Lyndon D. Estes | Kelly K. Caylor | Dee Luo | Stephanie R. Debats | Kelly K. Caylor | L. Estes | S. Debats | D. Luo
[1] Marco Madella,et al. Land-use classification , 2016 .
[2] Shiyoshi Yokoyama,et al. Land use classification with textural analysis and the aggregation technique using multi-temporal JERS-1 L-band SAR images , 2001 .
[3] Maria Cristina Rulli,et al. Global land and water grabbing , 2013, Proceedings of the National Academy of Sciences.
[4] A. Veldkamp,et al. utomated high resolution mapping of coffee in Rwanda using an xpert Bayesian network , 2014 .
[5] Senén Barro,et al. Do we need hundreds of classifiers to solve real world classification problems? , 2014, J. Mach. Learn. Res..
[6] P. Atkinson,et al. Incorporating Spatial Variability Measures in Land-cover Classification using Random Forest , 2011 .
[7] PE Pienaar,et al. Typology of smallholder farming in South Africa’s former homelands : towards an appropriate classification system , 2013 .
[8] Alan H. Strahler,et al. A note on procedures used for accuracy assessment in land cover maps derived from AVHRR data , 2000 .
[9] Lin Yan,et al. Automated crop field extraction from multi-temporal Web Enabled Landsat Data , 2014 .
[10] Melba M. Crawford,et al. Active Learning: Any Value for Classification of Remotely Sensed Data? , 2013, Proceedings of the IEEE.
[11] Cj Birch,et al. Rainfed Farming Systems , 2011 .
[12] J. Townshend,et al. Global land cover classifications at 8 km spatial resolution: The use of training data derived from Landsat imagery in decision tree classifiers , 1998 .
[13] Pinki Mondal,et al. Mapping cropping intensity of smallholder farms: A comparison of methods using multiple sensors , 2013 .
[14] D. Gale Johnson. Report on the 1950 World Census of Agriculture , 1956 .
[15] Johannes Roseboom,et al. UNLOCKING AFRICA ’ S AGRICULTURAL POTENTIAL , 2016 .
[16] Optimum Band Selection for Supervised Classification of Multispectral Data , 2007 .
[17] Steffen Fritz,et al. Cropland for sub‐Saharan Africa: A synergistic approach using five land cover data sets , 2011 .
[18] Howard J. Sanders,et al. Agriculture and Food , 1967 .
[19] J. Morton. The impact of climate change on smallholder and subsistence agriculture , 2007, Proceedings of the National Academy of Sciences.
[20] K. Price,et al. Optimal Landsat TM band combinations and vegetation indices for discrimination of six grassland types in eastern Kansas , 2002 .
[21] P. D’Odorico,et al. Food appropriation through large scale land acquisitions , 2014 .
[22] P. Koohafkan,et al. Enduring Farms: Climate Change, Smallholders and Traditional Farming Communities , 2008 .
[23] M. Ashton,et al. Hyperion, IKONOS, ALI, and ETM+ sensors in the study of African rainforests , 2004 .
[24] Piotr Tokarczyk,et al. Features, Color Spaces, and Boosting: New Insights on Semantic Classification of Remote Sensing Images , 2015, IEEE Transactions on Geoscience and Remote Sensing.
[25] Mahesh Pal,et al. Random forest classifier for remote sensing classification , 2005 .
[26] Roberta E. Martin,et al. A Tale of Two “Forests”: Random Forest Machine Learning Aids Tropical Forest Carbon Mapping , 2014, PloS one.
[27] Jos Boekhorst,et al. Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle? , 2012, Briefings Bioinform..
[28] John F. Mustard,et al. Forest cover change in Miombo Woodlands: modeling land cover of African dry tropical forests with linear spectral mixture analysis , 2015 .
[29] V. Rodriguez-Galiano,et al. Land cover change analysis of a Mediterranean area in Spain using different sources of data: Multi-seasonal Landsat images, land surface temperature, digital terrain models and texture , 2012 .
[30] Tom Fawcett,et al. An introduction to ROC analysis , 2006, Pattern Recognit. Lett..
[31] Antonio Criminisi,et al. Object Class Segmentation using Random Forests , 2008, BMVC.
[32] José Crespo,et al. Theoretical aspects of morphological filters by reconstruction , 1995, Signal Process..
[33] Joachim M. Buhmann,et al. Neuron geometry extraction by perceptual grouping in ssTEM images , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[34] Eric F. Wood,et al. Changing water availability during the African maize-growing season, 1979-2010 , 2014 .
[35] G. Alagarswamy,et al. Spatial variation of crop yield response to climate change in East Africa , 2009 .
[36] Joydeep Ghosh,et al. Investigation of the random forest framework for classification of hyperspectral data , 2005, IEEE Transactions on Geoscience and Remote Sensing.
[37] Calogero Carletto,et al. The Emperor has no Data ! Agricultural Statistics in Sub-Saharan Africa , 2013 .
[38] Rick Mueller,et al. Mapping global cropland and field size , 2015, Global change biology.
[39] Kirsten Halsnæs,et al. The development and climate nexus: the case of sub-Saharan Africa , 2003 .
[40] VekslerOlga,et al. Fast Approximate Energy Minimization via Graph Cuts , 2001 .
[41] Roberto Cipolla,et al. Semantic texton forests for image categorization and segmentation , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[42] Charles X. Ling,et al. Using AUC and accuracy in evaluating learning algorithms , 2005, IEEE Transactions on Knowledge and Data Engineering.
[43] J A Swets,et al. Measuring the accuracy of diagnostic systems. , 1988, Science.
[44] Nils Chr. Stenseth,et al. Sub‐saharan desertification and productivity are linked to hemispheric climate variability , 2001 .
[45] Christopher Conrad,et al. Analysis of uncertainty in multi-temporal object-based classification , 2015 .
[46] P. Atkinson,et al. Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture , 2012 .
[47] Thomas S. Jayne,et al. Urbanization and farm size in Asia and Africa: Implications for food security and agricultural research , 2013 .
[48] L. D. Estes,et al. A platform for crowdsourcing the creation of representative, accurate landcover maps , 2016, Environ. Model. Softw..
[49] D. R. Cutler,et al. Utah State University From the SelectedWorks of , 2017 .
[50] Andrew Newell,et al. Farm Size , 2009 .
[51] F. Rembold,et al. Assessing drought probability for agricultural areas in Africa with coarse resolution remote sensing imagery , 2011 .
[52] O. Dikshit,et al. Improvement of classification in urban areas by the use of textural features: The case study of Lucknow city, Uttar Pradesh , 2001 .
[53] Petra Döll,et al. Global Patterns of Cropland Use Intensity , 2010, Remote. Sens..
[54] Douglas Gollin,et al. An overview and implications for policy , 2014 .
[55] G. F. Hughes,et al. On the mean accuracy of statistical pattern recognizers , 1968, IEEE Trans. Inf. Theory.
[56] Mark A. Friedl,et al. Estimating pixel-scale land cover classification confidence using nonparametric machine learning methods , 2001, IEEE Trans. Geosci. Remote. Sens..
[57] O. B. Butusov,et al. Textural Classification of Forest Types from Landsat 7 Imagery , 2003 .
[58] Michael Oppenheimer,et al. Projected climate impacts to South African maize and wheat production in 2055: a comparison of empirical and mechanistic modeling approaches , 2013, Global change biology.
[59] Nilanjan Dey,et al. A survey of image classification methods and techniques , 2014, 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT).
[60] D. Roy,et al. Image interpretation-guided supervised classification using nested segmentation , 2015 .
[61] International Journal of Applied Earth Observation and Geoinformation , 2017 .
[62] Konrad Schindler,et al. Mapping of Agricultural Crops from Single High-Resolution Multispectral Images - Data-Driven Smoothing vs. Parcel-Based Smoothing , 2015, Remote. Sens..
[63] Alan H. Strahler,et al. Maximizing land cover classification accuracies produced by decision trees at continental to global scales , 1999, IEEE Trans. Geosci. Remote. Sens..
[64] Taloustieteen laitos,et al. Small-Scale Farmers in Liberalised Trade Environment , 2005 .
[65] W. Cohen,et al. Land cover mapping in an agricultural setting using multiseasonal Thematic Mapper data , 2001 .
[66] Jitendra Malik,et al. Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons , 2001, International Journal of Computer Vision.
[67] R. Colwell. Remote sensing of the environment , 1980, Nature.
[68] Robert A. Schowengerdt,et al. A review and analysis of backpropagation neural networks for classification of remotely-sensed multi-spectral imagery , 1995 .
[69] J. Paruelo,et al. Land cover classification in the Argentine Pampas using multi-temporal Landsat TM data , 2003 .
[70] R. Cook. The Population Reference Bureau , 1953 .
[71] Jon Atli Benediktsson,et al. Classification and feature extraction for remote sensing images from urban areas based on morphological transformations , 2003, IEEE Trans. Geosci. Remote. Sens..
[72] Steffen Fritz,et al. Identifying and quantifying uncertainty and spatial disagreement in the comparison of Global Land Cover for different applications , 2008 .
[73] Raymond Francis,et al. Field demonstration of an instrument performing automatic classification of geologic surfaces. , 2014, Astrobiology.
[74] Cordelia Schmid,et al. Constructing models for content-based image retrieval , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.
[75] Maria Cristina Rulli,et al. Land grabbing: a preliminary quantification of economic impacts on rural livelihoods , 2014, Population and environment.
[76] Thomas S. Jayne,et al. Land pressures, the evolution of farming systems, and development strategies in Africa: A synthesis , 2014 .
[77] Philip K. Thornton,et al. Agriculture and food systems in sub-Saharan Africa in a 4°C+ world , 2011, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[78] Steffen Fritz,et al. Improved global cropland data as an essential ingredient for food security , 2015 .
[79] J. Evans,et al. Modeling Species Distribution and Change Using Random Forest , 2011 .
[80] M. Hardy,et al. Rainfed Farming Systems in South Africa , 2011 .
[81] A. Prasad,et al. Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction , 2006, Ecosystems.
[82] Philip K. Thornton,et al. The potential impacts of climate change on maize production in Africa and Latin America in 2055 , 2003 .
[83] Paul A. Viola,et al. Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.
[84] Yang Shao,et al. Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points , 2012 .
[85] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[86] Johannes R. Sveinsson,et al. Random Forests for land cover classification , 2006, Pattern Recognit. Lett..
[87] Lorenzo Cotula,et al. Deal or no deal: the outlook for agricultural land investment in Africa , 2009 .
[88] Olga Veksler,et al. Fast approximate energy minimization via graph cuts , 2001, Proceedings of the Seventh IEEE International Conference on Computer Vision.
[89] Onisimo Mutanga,et al. Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers , 2014 .
[90] M. Walsh,et al. Identifying potential synergies and trade-offs for meeting food security and climate change objectives in sub-Saharan Africa , 2010, Proceedings of the National Academy of Sciences.
[91] David E. Bell,et al. Alliance for a Green Revolution in Africa (AGRA) , 2008 .
[92] Sucharita Gopal,et al. Uncertainty and Confidence in Land Cover Classification Using a Hybrid Classifier Approach , 2004 .
[93] MalikJitendra,et al. Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons , 2001 .
[94] Philip S. Yu,et al. Effective estimation of posterior probabilities: explaining the accuracy of randomized decision tree approaches , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).
[95] Stefan Walk,et al. BEYOND HAND-CRAFTED FEATURES IN REMOTE SENSING , 2013 .
[96] Nathalie A. Cabrol,et al. Smart, texture‐sensitive instrument classification for in situ rock and layer analysis , 2013 .
[97] S. Goetz,et al. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps , 2012 .
[98] Qihao Weng,et al. A survey of image classification methods and techniques for improving classification performance , 2007 .
[99] Giles M. Foody,et al. Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification , 2004 .
[100] Brief Oksana Nagayets. Small farms : Current Status and Key Trends Information , 2005 .
[101] D. L. Seen,et al. Mapping Fragmented Agricultural Systems in the Sudano-Sahelian Environments of Africa Using Random Forest and Ensemble Metrics of Coarse Resolution MODIS Imagery , 2012 .
[102] Scott N. Miller,et al. High-resolution landcover classification using Random Forest , 2014 .
[103] Vladimir Kolmogorov,et al. An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision , 2001, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[104] R. Dwivedi,et al. Textural analysis of IRS-1D panchromatic data for land cover classification , 2002 .
[105] Alan H. Strahler,et al. Fuzzy Neural Network Classification of Global Land Cover from a 1° AVHRR Data Set , 1999 .
[106] Franklin C. Crow,et al. Summed-area tables for texture mapping , 1984, SIGGRAPH.
[107] Andrew Newell,et al. Chapter 65 Farm Size , 2010 .
[108] Pierre Defourny,et al. A conceptual framework to define the spatial resolution requirements for agricultural monitoring using remote sensing , 2010 .
[109] Kent A. Spackman,et al. Signal Detection Theory: Valuable Tools for Evaluating Inductive Learning , 1989, ML.
[110] Mario Chica-Olmo,et al. An assessment of the effectiveness of a random forest classifier for land-cover classification , 2012 .
[111] Xinhua Zhuang,et al. Image Analysis Using Mathematical Morphology , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[112] G. Shao,et al. Mapping of boreal vegetation of a temperate mountain in China by multitemporal Landsat TM imagery , 2002 .
[113] S. Saatchi,et al. Application of multiscale texture in classifying JERS-1 radar data over tropical vegetation , 2002 .
[114] Richard Szeliski,et al. Computer Vision - Algorithms and Applications , 2011, Texts in Computer Science.
[115] David B. Lobell,et al. The use of satellite data for crop yield gap analysis , 2013 .